Usually based on molecular mechanics force fields, the post-optimization of ligand poses is typically the most time-consuming step in protein–ligand docking procedures. In return, it bears the potential to overcome the limitations of discretized conformation models. Because of the parallel nature of the problem, recent graphics processing units (GPUs) can be applied to address this dilemma. We present a novel algorithmic approach for parallelizing and thus massively speeding up protein–ligand complex optimizations with GPUs. The method, customized to pose-optimization, performs at least 100 times faster than widely used CPU-based optimization tools. An improvement in Root-Mean-Square Distance (RMSD) compared to the original docking pose of up to 42% can be achieved. © 2012 Wiley Periodicals, Inc.
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